NRLPapers

Must-read papers on NRL/NE.

NRL: network representation learning. NE: network embedding.

Contributed by Cunchao Tu, Yuan Yao, Zhengyan Zhang, GanquCui, Hao Wang (BUPT), Changxin Tian (BUPT), Jie Zhou and Cheng Yang (BUPT).

We release OpenNE, an open source toolkit for NE/NRL. This repository provides a standard NE/NRL(Network Representation Learning）training and testing framework. Currently, the implemented models in OpenNE include DeepWalk, LINE, node2vec, GraRep, TADW and GCN.

Content

1. Survey Papers
2. Models
   1. Basic Models
   2. Attributed Network
   3. Dynamic Network
   4. Heterogeneous Information Network
   5. Bipartite Network
   6. Directed Network
   7. Other Models
3. Applications
   1. Natural Language Processing
   2. Knowledge Graph
   3. Social Network
   4. Graph Clustering
   5. Community Detection
   6. Recommendation
   7. Other Applications

Survey Papers

1. Representation Learning on Graphs: Methods and Applications. William L. Hamilton, Rex Ying, Jure Leskovec. IEEE Data(base) Engineering Bulletin 2017. paper

2. Graph Embedding Techniques, Applications, and Performance: A Survey. Palash Goyal, Emilio Ferrara. Knowledge Based Systems 2017. paper

3. A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications. Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang. TKDE 2017. paper

4. Network Representation Learning: A Survey. Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang. IEEE Transactions on Big Data 2018. paper

5. A Tutorial on Network Embeddings. Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena. arxiv 2018. paper

6. Network Representation Learning: An Overview.(In Chinese) Cunchao Tu, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2017. paper

7. Relational inductive biases, deep learning, and graph networks. Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu. arxiv 2018. paper

Models

Basic Models

1. SepNE: Bringing Separability to Network Embedding. Ziyao Li, Liang Zhang, Guojie Song. AAAI 2019. paper

2. Robust Negative Sampling for Network Embedding. Mohammadreza Armandpour, Patrick Ding, Jianhua Huang, Xia Hu. AAAI 2019. paper

3. Network Structure and Transfer Behaviors Embedding via Deep Prediction Model. Xin Sun, Zenghui Song, Junyu Dong, Yongbo Yu, Claudia Plant, Christian Böhm. AAAI 2019. paper

4. Simplifying Graph Convolutional Networks. Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger. ICML 2019. paper

5. GMNN: Graph Markov Neural Networks. Meng Qu, Yoshua Bengio, Jian Tang. ICML 2019. paper

6. Stochastic Blockmodels meet Graph Neural Networks. Nikhil Mehta, Lawrence Carin Duke, Piyush Rai. ICML 2019. paper

7. Disentangled Graph Convolutional Networks. Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu. ICML 2019. paper

8. Position-aware Graph Neural Networks. Jiaxuan You, Rex Ying, Jure Leskovec. ICML 2019. paper

9. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan. ICML 2019. paper

10. Graph U-Nets. Hongyang Gao, Shuiwang Ji. ICML 2019. paper

11. Self-Attention Graph Pooling. Junhyun Lee, Inyeop Lee, Jaewoo Kang. ICML 2019. paper

12. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. Aleksandar Bojchevski, Stephan Günnemann. ICLR 2018. paper

13. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. Jie Chen, Tengfei Ma, Cao Xiao. ICLR 2018. paper

14. Graph Attention Networks. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio. ICLR 2018. paper

15. Stochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song. ICML 2018. paper

16. Adversarially Regularized Graph Autoencoder for Graph Embedding. Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang. IJCAI 2018. paper

17. Discrete Network Embedding. Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew-Soon Ong, Quan-Sen Sun. IJCAI 2018. paper

18. Feature Hashing for Network Representation Learning. Qixiang Wang, Shanfeng Wang, Maoguo Gong, Yue Wu. IJCAI 2018. paper

19. Deep Inductive Network Representation Learning. Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed. WWW 2018. paper

20. Active Discriminative Network Representation Learning. Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, Yue Hu. IJCAI 2018. paper

21. MILE: A Multi-Level Framework for Scalable Graph Embedding. Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy. arxiv 2018. paper

22. Out-of-sample extension of graph adjacency spectral embedding. Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe. ICML 2018. paper

23. DeepWalk: Online Learning of Social Representations. Bryan Perozzi, Rami Al-Rfou, Steven Skiena. KDD 2014. paper code

24. Non-transitive Hashing with Latent Similarity Componets. Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu. KDD 2015. paper

25. GraRep: Learning Graph Representations with Global Structural Information. Shaosheng Cao, Wei Lu, Qiongkai Xu. CIKM 2015. paper code

26. LINE: Large-scale Information Network Embedding. Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Me. WWW 2015. paper code

27. Deep Neural Networks for Learning Graph Representations. Shaosheng Cao, Wei Lu, Xiongkai Xu. AAAI 2016. paper code

28. Revisiting Semi-supervised Learning with Graph Embeddings. Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov. ICML 2016. paper

29. Max-Margin DeepWalk: Discriminative Learning of Network Representation. Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun. IJCAI 2016. paper code

30. Discriminative Deep RandomWalk for Network Classification. Juzheng Li, Jun Zhu, Bo Zhang. ACL 2016. paper

31. Structural Deep Network Embedding. Daixin Wang, Peng Cui, Wenwu Zhu. KDD 2016. paper

32. Structural Neighborhood Based Classification of Nodes in a Network. Sharad Nandanwar, M. N. Murty. KDD 2016. paper

33. Community Preserving Network Embedding. Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang. AAAI 2017. paper

34. Semi-supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. ICLR 2017. paper code

35. Fast Network Embedding Enhancement via High Order Proximity Approximation. Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu. IJCAI 2017. paper [code](https://git